grpo-rl-training

Apply GRPO/RL training with TRL to optimize language models.

52|6|Updated Nov 24, 2025

Search Tags:#TRL #LoRA #GRPO #transformers #RL training #reward functions

Ready to run

npx skills add https://github.com/ovachiever/droid-tings --skill grpo-rl-training

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: grpo-rl-training
Source: https://github.com/ovachiever/droid-tings/tree/main/skills/grpo-rl-training
Command: npx skills add https://github.com/ovachiever/droid-tings --skill grpo-rl-training

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires transformers>=4.47.0, trl>=0.14.0, datasets>=3.2.0, peft>=0.14.0, torch, and includes references (resource) components.

What problem does it solve?

Expert patterns and templates for Group Relative Policy Optimization (GRPO) training using TRL, enabling robust reward design and production-ready workflows.

Core Features & Use Cases

Production-ready templates for GRPO training (LoRA, reward composition, evaluation)
Reward function library with multiple reward shapes (format, correctness, style)
End-to-end workflow guidance from dataset prep to deployment

Quick Start

Use the production-ready template at templates/basic_grpo_training.py and customize rewards for your task.

Frequently Asked Questions about grpo-rl-training

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I apply GRPO training to optimize a language model?▼

GRPO training uses Group Relative Policy Optimization with TRL to fine-tune language models by comparing model outputs within groups and optimizing against reward signals. The Skill provides production-ready templates and reward libraries to set up end-to-end workflows from dataset preparation through evaluation and deployment.

What reward functions should I design for GRPO training?▼

Reward functions in GRPO can target format correctness, style adherence, or multi-objective goals depending on your task. This Skill includes a library of reward shapes for common scenarios like math reasoning, code generation, fact-checking, Q&A, and formatting control, with templates for composing multiple rewards together.

Can I use LoRA parameterization with GRPO training?▼

Yes. GRPO training with TRL and PEFT supports LoRA parameterization, reducing memory and compute requirements during fine-tuning. This Skill includes production-grade templates that integrate LoRA configuration directly into the training workflow.

How do I prepare datasets for GRPO RL training workflows?▼

Dataset preparation for GRPO involves structuring ground-truth data and prompt templates to support within-group comparisons and reward evaluation. This Skill covers dataset formatting, integration with the transformers and datasets libraries, and validation steps before launching training.

What does the complete GRPO training pipeline look like from start to deployment?▼

The pipeline spans dataset preparation, reward function design, training configuration with TRL, evaluation metrics, and deployment. This Skill provides end-to-end guidance and ready-to-use templates addressing each stage, enabling rapid prototyping across multiple task scenarios.

What are the limitations of GRPO compared to other RL training approaches?▼

GRPO requires careful reward design and within-group comparison structure; misaligned rewards or small group sizes can degrade performance. This Skill documents these constraints and provides best practices for reward composition and training configuration to mitigate common pitfalls.